[PocketFlow]解决在coco上mAP非常低的bug
1.问题
继上次训练挂起的bug后，又遇到了现在评估时AP非常低的bug。具体有多低呢？Pelee论文中提到，用128的batchsize大小在coco数据集上训练70K次迭代后，AP@0.5:0.95为22.4，而我用32的batchsize反复微调之后，最后AP也只从2.9上升到了3.7...下图为训练的过程：
2.解决
其实看loss和accuracy还是可以的，但是ap就是上不去，粗略想到了4个地方可能存在的问题：
- 训练有误
因为尝试了各种学习率，而且在各个学习率下都是训练到ap和loss都不变之后才改变学习率，因此我想不出训练还有什么其他花样了...排除 - 数据有误
因为coco的数据是自己转换的，说实话对这块还是有点不放心的。况且之前训练的bug已经检查过数据了，看样子是没什么问题。现在再检查工作量也比较大，存疑，先放着 - 模型有误
模型在voc数据集上能成功复现，排除 - 计算有误
因为coco评估的脚本是在voc上改的，生成json文件之后再使用官方的cocoapi计算。所以有很大可能是生成json文件的脚本哪里写错了
综上，先排查评估是否有计算错误。
但是没看出来...于是，我寻思着在原来的voc_eval.py文件上做一些修改，以适配coco，再用voc的方式评估。虽然计算方式有差别，但不会差太远。如果ap变化不大，那说明计算方式没有问题，需要检查数据(最怕的就是这种情况，因为工作量很大，而且数据也容易出错，还好gt标错的坑已经提前踩了，想想就可怕)；反之就是评估计算有问题。
# voc_eval.py 程序结构
def do_python_eval(dataset_path, pred_path, use_07=True):
aps = []
#对每个类别：执行
rec, prec, ap = voc_eval(filename, # 每个类别的预测文件result_x.txt
os.path.join(dataset_path, anno_files), # 每张图片的标注文件
os.path.join(dataset_path, all_images_file), # 所有图片的文件名文件
cls_name, # 类别名
cache_path, # 用于暂存所有图片的标注
ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
#将rec, prec和ap 存到对应类别的 xx_pr.pkl文件
#打印AP和mAP
def voc_eval(detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=True):
#1.从imagesetfile中读取所有图片文件名
#2.如果cachedir中的annots.pkl文件不存在，则将标注按文件名打包之后写入annots.pkl；否则加载文件到recs字典中（key为文件名）
#3.提取该类别的标注，按文件名打包存到class_recs字典中
#4.从txt文件读取该类别的标注
#5.计算rec, prec, 用voc_ap计算ap
#6.返回rec, prec, ap
#如果要更改以适配COCO，需要：
#获取所有image_id
#从minival.json中按image_id提取标注
分析了一波之后改好了代码，也能正常运行（非常慢）。要命的是好不容易把文件都读完了，结果报错了。显示keyerror：
{213035: [{'name': 'scissors', 'bbox': [314.25, 168.05, 79.57, 53.75]}, {'name': 'scissors', 'bbox': [238.06, 170.62, 89.75, 64.11]}, {'name': 'person', 'bbox': [0.0, 110.12, 311.59, 235.62]}, {'name': 'person', 'bbox': [195.75, 2.88, 260.04, 109.39]}, {'name': 'bowl', 'bbox': [177.04, 0.0, 66.69, 135.89]}, {'name': 'person', 'bbox': [305.0, 53.24, 330.51, 373.76]}]}
r = [obj for obj in dic[213035] if obj['name'] == 'person']
# 大致是说obj没有name属性
输出改obj的iamge_id之后到minival.json去找，发现这个标注的类别是82还是83...coco不是80个类吗？给我整懵逼了...
但是，突然灵光一闪，我似乎已经找到问题所在了。coco数据集虽然有80个类，但是却不是顺序排下来的，中间有跳过的序号，所以真实的序号是从1到90，这个项目之前做过一个转换：
labelmap = {
"none_of_the_above": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"10": 10,
"11": 11,
"13": 12,
"14": 13,
"15": 14,
"16": 15,
"17": 16,
"18": 17,
"19": 18,
"20": 19,
"21": 20,
"22": 21,
"23": 22,
"24": 23,
"25": 24,
"27": 25,
"28": 26,
"31": 27,
"32": 28,
"33": 29,
"34": 30,
"35": 31,
"36": 32,
"37": 33,
"38": 34,
"39": 35,
"40": 36,
"41": 37,
"42": 38,
"43": 39,
"44": 40,
"46": 41,
"47": 42,
"48": 43,
"49": 44,
"50": 45,
"51": 46,
"52": 47,
"53": 48,
"54": 49,
"55": 50,
"56": 51,
"57": 52,
"58": 53,
"59": 54,
"60": 55,
"61": 56,
"62": 57,
"63": 58,
"64": 59,
"65": 60,
"67": 61,
"70": 62,
"72": 63,
"73": 64,
"74": 65,
"75": 66,
"76": 67,
"77": 68,
"78": 69,
"79": 70,
"80": 71,
"81": 72,
"82": 73,
"84": 74,
"85": 75,
"86": 76,
"87": 77,
"88": 78,
"89": 79,
"90": 80
}
COCO_LABELS = {
"bench": (14, 'outdoor'),
"skateboard": (37, 'sports'),
"toothbrush": (80, 'indoor'),
"person": (1, 'person'),
"donut": (55, 'food'),
"none": (0, 'background'),
"refrigerator": (73, 'appliance'),
"horse": (18, 'animal'),
"elephant": (21, 'animal'),
"book": (74, 'indoor'),
"car": (3, 'vehicle'),
"keyboard": (67, 'electronic'),
"cow": (20, 'animal'),
"microwave": (69, 'appliance'),
"traffic light": (10, 'outdoor'),
"tie": (28, 'accessory'),
"dining table": (61, 'furniture'),
"toaster": (71, 'appliance'),
"baseball glove": (36, 'sports'),
"giraffe": (24, 'animal'),
"cake": (56, 'food'),
"handbag": (27, 'accessory'),
"scissors": (77, 'indoor'),
"bowl": (46, 'kitchen'),
"couch": (58, 'furniture'),
"chair": (57, 'furniture'),
"boat": (9, 'vehicle'),
"hair drier": (79, 'indoor'),
"airplane": (5, 'vehicle'),
"pizza": (54, 'food'),
"backpack": (25, 'accessory'),
"kite": (34, 'sports'),
"sheep": (19, 'animal'),
"umbrella": (26, 'accessory'),
"stop sign": (12, 'outdoor'),
"truck": (8, 'vehicle'),
"skis": (31, 'sports'),
"sandwich": (49, 'food'),
"broccoli": (51, 'food'),
"wine glass": (41, 'kitchen'),
"surfboard": (38, 'sports'),
"sports ball": (33, 'sports'),
"cell phone": (68, 'electronic'),
"dog": (17, 'animal'),
"bed": (60, 'furniture'),
"toilet": (62, 'furniture'),
"fire hydrant": (11, 'outdoor'),
"oven": (70, 'appliance'),
"zebra": (23, 'animal'),
"tv": (63, 'electronic'),
"potted plant": (59, 'furniture'),
"parking meter": (13, 'outdoor'),
"spoon": (45, 'kitchen'),
"bus": (6, 'vehicle'),
"laptop": (64, 'electronic'),
"cup": (42, 'kitchen'),
"bird": (15, 'animal'),
"sink": (72, 'appliance'),
"remote": (66, 'electronic'),
"bicycle": (2, 'vehicle'),
"tennis racket": (39, 'sports'),
"baseball bat": (35, 'sports'),
"cat": (16, 'animal'),
"fork": (43, 'kitchen'),
"suitcase": (29, 'accessory'),
"snowboard": (32, 'sports'),
"clock": (75, 'indoor'),
"apple": (48, 'food'),
"mouse": (65, 'electronic'),
"bottle": (40, 'kitchen'),
"frisbee": (30, 'sports'),
"carrot": (52, 'food'),
"bear": (22, 'animal'),
"hot dog": (53, 'food'),
"teddy bear": (78, 'indoor'),
"knife": (44, 'kitchen'),
"train": (7, 'vehicle'),
"vase": (76, 'indoor'),
"banana": (47, 'food'),
"motorcycle": (4, 'vehicle'),
"orange": (50, 'food')
}
妈的生成json文件的时候我忘了换回来了（其实想一下好像不这样来回转也行）...所以只有序号从1到11的类别能够对上。简单修改后，得到真正的预测结果:
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.199
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.343
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.201
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.030
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.200
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.365
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.201
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.295
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.314
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.054
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.342
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.548
与论文中的22.4和38.3相比还有差距，但是至少没有开始吓人了，再根据正确的AP微调一下应该还能提高点。
算是误打误撞解决了？
虽然这次的bug也调了一星期，但是明显没有上次那么慌了。
